The amount of media content (e.g., video content) available on mobile devices is growing. However, a network often cannot provide constant bandwidth to a mobile device to play the media content over the network. As network speed and/or available bandwidth from a network vary, the quality of video content received on a mobile device can be reduced. One solution is to implement adaptive bitrate (ABR) streaming which can be implemented to deliver different quality media content to a wide range of devices based on available network connections and available bandwidth (e.g., the quality of video provided to a mobile device can be varied based on the available bandwidth of a network). For example, a lower bitrate and lower video quality can be presented to a mobile device in cases where a network connection offers low bandwidth or when network bandwidth drops. In situations where the network becomes congested during playback the bandwidth of the stream can be reduced to deliver lower quality video but to maintain the playout of the stream and mitigate any buffering of the stream which pauses play-out to the user. Higher quality video can be resumed once network congestion has eased. Similarly, a higher bitrate and higher video quality can be presented to a mobile device for higher available network bandwidth.
In order to ensure a high degree of satisfaction for the user of video services such as adaptive video streaming, the perceived video quality of those services needs to be estimated. It is a major responsibility of the broadcast provider towards both content provider and customer to maintain the quality of its service. In all of the cases described above the perceived quality of the video stream viewed by the user will be impacted and it is important for the broadcast providers to understand this impact on quality of experience metrics to be able to manage the network in order to provide the best possible experience to the end consumer. The quality of experience provided may be particularly important for paid services (e.g., whether paid via end consumer subscriptions or the like, or through advertising associated with the distributed media). When using ABR streaming because the actual quality adaptations may occur over longer periods of time (several minutes long rather than several seconds) it is much more difficult to understand the impact on overall quality. Combined with this is the impact of re-buffering events that can transpire when network congestion occurs and before adaptation to a tolerable lower quality stream has been completed. Taking into account all of these subjective factors to predict the overall perception of the end-user is currently difficult because of the limited subjective data available in this new area of video streaming.
Common video quality monitoring models, such as the ITU-T Recommendations P.1201 and P.1202 models do not take into account the aforementioned quality adaptation events. The ITU-T P.1201 model operates by analyzing packet header information as available from respective packet trace data, while the ITU-T P.1202 models exploit further bit stream information, such as coding-related information. Thus, to ensure end consumer satisfaction, the broadcasters of ABR streaming typically need to proactively monitor the combined quality of experience metrics which can accurately reflect the overall user experience.